{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:43:07Z","timestamp":1771486987448,"version":"3.50.1"},"reference-count":16,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"PERSEUS project","doi-asserted-by":"publisher","award":["2023-68012-38992"],"award-info":[{"award-number":["2023-68012-38992"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"PERSEUS project","doi-asserted-by":"publisher","award":["2417510"],"award-info":[{"award-number":["2417510"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"PERSEUS project","doi-asserted-by":"publisher","award":["2412928"],"award-info":[{"award-number":["2412928"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"PERSEUS project","doi-asserted-by":"publisher","award":["1032382"],"award-info":[{"award-number":["1032382"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"PERSEUS project","doi-asserted-by":"publisher","award":["1032672"],"award-info":[{"award-number":["1032672"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF","award":["2023-68012-38992"],"award-info":[{"award-number":["2023-68012-38992"]}]},{"name":"NSF","award":["2417510"],"award-info":[{"award-number":["2417510"]}]},{"name":"NSF","award":["2412928"],"award-info":[{"award-number":["2412928"]}]},{"name":"NSF","award":["1032382"],"award-info":[{"award-number":["1032382"]}]},{"name":"NSF","award":["1032672"],"award-info":[{"award-number":["1032672"]}]},{"name":"USDA NIFA","award":["2023-68012-38992"],"award-info":[{"award-number":["2023-68012-38992"]}]},{"name":"USDA NIFA","award":["2417510"],"award-info":[{"award-number":["2417510"]}]},{"name":"USDA NIFA","award":["2412928"],"award-info":[{"award-number":["2412928"]}]},{"name":"USDA NIFA","award":["1032382"],"award-info":[{"award-number":["1032382"]}]},{"name":"USDA NIFA","award":["1032672"],"award-info":[{"award-number":["1032672"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (Quercus rubra), hackberry (Celtis occidentalis), and bitternut hickory (Carya cordiformis) using the CentralBark dataset. We investigated three environmental variables\u2014time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation\u2014to identify sources of classification inaccuracies. Results revealed that bark moisture significantly reduced accuracy by 8.19% in wet conditions (89.32% dry vs. 81.13% wet). In comparison, the time of day had a significant impact on hackberry (95.56% evening) and northern red oak (80.80% afternoon), with notable chi-squared associations (p &lt; 0.05). Cardinal direction had minimal effect (4.72% variation). Bitternut hickory detection consistently underperformed (26.76%), highlighting morphological challenges. These findings underscore the need for targeted dataset augmentation with wet and afternoon images, alongside preprocessing techniques like illumination normalization, to improve model robustness. Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems.<\/jats:p>","DOI":"10.3390\/a18070417","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T02:25:53Z","timestamp":1751941553000},"page":"417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Environmental Sensitivity in AI Tree Bark Detection: Identifying Key Factors for Improving Classification Accuracy"],"prefix":"10.3390","volume":"18","author":[{"given":"Charles","family":"Warner","sequence":"first","affiliation":[{"name":"Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47906, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4894-5738","authenticated-orcid":false,"given":"Fanyou","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47906, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rado","family":"Gazo","sequence":"additional","affiliation":[{"name":"Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47906, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5293-2112","authenticated-orcid":false,"given":"Bedrich","family":"Benes","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2772-0166","authenticated-orcid":false,"given":"Songlin","family":"Fei","sequence":"additional","affiliation":[{"name":"Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47906, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"ref_1","unstructured":"Fiel, S., and Sablatnig, R. (2010). Tree Species Dataset consisting of Images of the Bark, Leaves or Needles [Data set]. Zenodo."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_3","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Carpentier, M., Giguere, P., and Gaudreault, J. (2018, January 1\u20135). Tree Species Identification from Bark Images Using Convolutional Neural Networks. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593514"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cui, Z., Li, X., Li, T., and Li, M. (2023). 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Proceedings of the Computer Vision\u2014ECCV 2020 Workshops, Glasgow, UK.","DOI":"10.1007\/978-3-030-65414-6_15"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Warner, C., Wu, F., Gazo, R., Benes, B., Kong, N., and Fei, S. (2024). CentralBark Image Dataset and Tree Species Classification Using Deep Learning. Algorithms, 17.","DOI":"10.3390\/a17050179"},{"key":"ref_10","unstructured":"Tan, M., and Le, Q.V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019). Searching for MobileNetV3. arXiv.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_13","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Weeks, S.S., Weeks, H.P., and Parker, G.R. (2010). Native Trees of the Midwest: Identification, Wildlife Values, and Land-scaping Use, Purdue University Press. [2nd ed.].","DOI":"10.2307\/j.ctv15wxr20"},{"key":"ref_15","unstructured":"Wojtech, M., and Wessels, T. (2011). Bark: A Field Guide to Trees of the Northeast, Brandeis University Press."},{"key":"ref_16","first-page":"1","article-title":"Forestry applications of UAVs in Europe: A review","volume":"38","author":"Torresan","year":"2016","journal-title":"Int. J. Remote Sens."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/7\/417\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:06:12Z","timestamp":1760033172000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/7\/417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":16,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["a18070417"],"URL":"https:\/\/doi.org\/10.3390\/a18070417","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,8]]}}}